The diagnostically superior information available from data acquired from various imaging systems, especially that provided by multidetector CT (multiple slices acquired per single rotation of the gantry) where acquisition speed and volumetric resolution provide exquisite diagnostic value, enables the detection of potential problems at earlier and more treatable stages. Given the vast quantity of detailed data acquirable from imaging systems, various algorithms must be developed to efficiently and accurately process image data. With the aid of computers, advances in image processing are generally performed on digital or digitized images.
Digital acquisition systems for creating digital images include digital X-ray radiography, computed tomography (“CT”) imaging, magnetic resonance imaging (“MRI”) and nuclear medicine imaging techniques, such as positron emission tomography (“PET”) and single photon emission computed tomography (“SPECT”). Digital images can also be created from analog images by, for example, scanning analog images, such as typical x-ray films, into a digitized form. Further information concerning digital acquisition systems is found in the above-referenced copending application “Graphical User Interface for Display of Anatomical Information”.
Digital images are created from an array of numerical values representing a property (such as a radiation intensity or magnetic field strength) associable with an anatomical location referenced by a particular array location. In 2-D digital images, or slice sections, the discrete array locations are termed pixels. Three-dimensional digital images can be constructed from stacked slice sections through various construction techniques known in the art. The 3-D images are made up of discrete volume elements, also referred to as voxels, composed of pixels from the 2-D images. The pixel or voxel properties can be processed to ascertain various properties about the anatomy of a patient associated with such pixels or voxels.
Once in a digital or digitized format, various analytical approaches can be applied to process digital anatomical images and to detect, identify, display and highlight regions of interest (ROI). For example, digitized images can be processed through various techniques, such as segmentation. Segmentation generally involves separating irrelevant objects (for example, the background from the foreground) or extracting anatomical surfaces, structures, or regions of interest from images for the purposes of anatomical identification, diagnosis, evaluation, and volumetric measurements. Segmentation often involves classifying and processing, on a per-pixel basis, pixels of image data on the basis of one or more characteristics associable with a pixel value. For example, a pixel or voxel may be examined to determine whether it is a local maximum or minimum based on the intensities of adjacent pixels or voxels.
Once anatomical regions and structures are constructed and evaluated by analyzing pixels and/or voxels, subsequent processing and analysis exploiting regional characteristics and features can be applied to relevant areas, thus improving both accuracy and efficiency of the imaging system. For example, the segmentation of an image into distinct anatomical regions and structures provides perspectives on the spatial relationships between such regions. Segmentation also serves as an essential first stage of other tasks such as visualization and registration for temporal and cross-patient comparisons.
Key issues in digital image processing are speed and accuracy. For example, the size of a detectable tumor or nodule, such as a lung nodule, can be smaller than 2 mm in diameter. As a result, an axial section that might be used in detecting such a tumor would typically be a 512×512 array of pixels having a spatial resolution of 500 microns. Moreover, depending on the particular case, a typical volume data set can include several hundred axial sections, making the total amount of data 200 Megabytes or more. In addition, the total data set might include several volume sets, each taken at a different time. Thus, due to the sheer size of such data sets and the desire to identify small artifacts, computational efficiency and accuracy is of high priority to satisfy the throughput requirements of any digital processing method or system.
Thus, it is desirable to provide segmentation systems and methods for segmenting images that are not computationally intensive. It is also desirable that the segmentation systems and methods support various data acquisition systems, such as MRI, CT, PET or SPECT scanning and imaging. It is further desirable to provide segmentation systems and methods that support temporal and cross-patient comparisons and that provide accurate results for diagnosis. It is desirable to provide segmentation systems and methods for registering images that can handle 2-D and 3-D data sets. It is desirable to provide a segmentation approach that can be performed on partial volumes to reduce processing loads and patient radiation doses. It is further desirable to provide a segmentation process that provides results displayable on a computer display or that can be printed to support medical diagnosis and evaluation. The present invention provides a system and method that are accurate, flexible and display high levels of physiological detail over the prior art without specially configured equipment.